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Beyond BCI: Validating a wireless, consumer-grade EEG headset against a medical-grade system for evaluating EEG effects of a test anxiety intervention in school
Citation
Khng, K. H., & Mane, R. (2020). Beyond BCI: Validating a wireless, consumer-grade EEG headset against a medical-grade system for evaluating EEG effects of a test anxiety intervention in school. Advanced Engineering Informatics, 45, Article 101106. https://doi.org/10.1016/j.aei.2020.101106
Abstract
Educational neuroscience is an emerging interdisciplinary field. However, the use of neuroimaging techniques and tools such as electroencephalography (EEG) in school-based interventions and research is limited, largely due to the high costs and physical constraints of conventional research- or medical-grade equipment. Neuroimaging and electrophysiological data can provide useful evidence to validate the efficacy of interventions. The present study explores the utility of lightweight, affordable, and easy-toset-up EEG systems for use in school-based research with children. Specifically, we examine the effects of a deep-breathing-for-test-anxiety intervention on brain electrical activity during a flanker distractor interference task in eleven-year olds, comparing the pattern of results observed using a consumer-grade EEG system (Emotiv EPOC+) against that obtained using a medical-grade EEG system (Neurostyle). Behavioral, EEG, and respiratory data was obtained from Primary 5 students (N = 45; Mage = 10.88, SD = .33), split into Emotiv and Neurostyle groups. The aim of the study was two-fold: to examine the effects of deep breathing on neurophysiological and behavioral correlates of inhibitory control of attention in children, and to understand the affordances and limitations of the Emotiv system for school-based research with children. Results from power spectral analyses suggest that deep breathing may enhance attentional control on a neural level by modulating brain electrical activity on several frequencies. Despite limitations, the low-res, consumer-grade EEG system appears to be capable of detecting some degree of power spectral differences associated with intervention effects.
Date Issued
2020
Publisher
Elsevier
Journal
Advanced Engineering Informatics